{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Check: when model behaviours should overlap\n",
    "\n",
    "Many of the model classes in GPflow have overlapping behaviour in special cases. In this section, we fit some approximations to a model with a Gaussian likelihood, and make sure they're all the same. \n",
    "\n",
    "The models are:\n",
    " - `GPR`: full Gaussian process regression\n",
    " \n",
    " - `VGP`: a Gaussian approximation with Variational Bayes\n",
    "   (approximating a Gaussian posterior with a Gaussian should be exact)\n",
    "   \n",
    " - `SVGP`: a sparse GP, with a Gaussian approximation. The inducing points are set to be at the data points, so again, should be exact. \n",
    " \n",
    " - `SVGP` (with whitened representation): As above, but with a rotation applied to whiten the representation of the process\n",
    " \n",
    " - `SGPR`: A sparse GP with a *collapsed* posterior (Titsias 2009). Again, the inducing points are fixed to the data points. \n",
    " \n",
    " - `GPRFITC`: The FITC approximation; again, the inducing points are fixed to the data points.\n",
    " \n",
    "In all cases the parameters are estimated by the method of maximum likelihood (or approximate maximum likelihood, as appropriate). The parameter estimates should all be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpflow\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
    "plt = matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "X = np.random.rand(20,1)*10\n",
    "Y = np.sin(X) + 0.9 * np.cos(X*1.6) + np.random.randn(*X.shape)* 0.4\n",
    "Xtest = np.random.rand(10,1)*10\n",
    "plt.plot(X, Y, 'kx', mew=2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3528: setdiff1d (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2018-11-30.\n",
      "Instructions for updating:\n",
      "This op will be removed after the deprecation date. Please switch to tf.sets.difference().\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3528: setdiff1d (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2018-11-30.\n",
      "Instructions for updating:\n",
      "This op will be removed after the deprecation date. Please switch to tf.sets.difference().\n"
     ]
    }
   ],
   "source": [
    "m1 = gpflow.models.GPR(X, Y, kern=gpflow.kernels.RBF(1))\n",
    "m2 = gpflow.models.VGP(X, Y, gpflow.kernels.RBF(1), likelihood=gpflow.likelihoods.Gaussian())\n",
    "m3 = gpflow.models.SVGP(X, Y, gpflow.kernels.RBF(1),\n",
    "                      likelihood=gpflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False)\n",
    "m3.feature.set_trainable(False)\n",
    "m4 = gpflow.models.SVGP(X, Y, gpflow.kernels.RBF(1),\n",
    "                      likelihood=gpflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False, whiten=True)\n",
    "m4.feature.set_trainable(False)\n",
    "m5 = gpflow.models.SGPR(X, Y, gpflow.kernels.RBF(1), Z=X.copy())\n",
    "m5.feature.set_trainable(False)\n",
    "m6 = gpflow.models.GPRFITC(X, Y, gpflow.kernels.RBF(1), Z=X.copy())\n",
    "m6.feature.set_trainable(False)\n",
    "models = [m1, m2, m3, m4, m5, m6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now optimize the models. For GPR, SVGP and GPRFITC this simply optimizes the hyperparameters (since the inducing points are fixed). For the variational models, this jointly maximises the lower bound to the marginal likelihood (Evidence Lower Bound, ELBO) with respect to the variational parameters and the kernel and likelihood hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/st/anaconda3/envs/relaxedgp/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 71\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 52\n",
      "  Number of functions evaluations: 70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834355\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834355\n",
      "  Number of iterations: 7\n",
      "  Number of functions evaluations: 12\n"
     ]
    }
   ],
   "source": [
    "o = gpflow.train.ScipyOptimizer(method='BFGS')\n",
    "_ = [o.minimize(m, maxiter=100) for m in models]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything worked as planned, the models should have the same\n",
    "\n",
    " - prediction functions\n",
    " - log (marginal) likelihood\n",
    " - kernel parameters\n",
    " \n",
    "For the variational models, where we use a ELBO in place of the likelihood, the ELBO should be tight to the likelihood in the cases studied here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(m, color, ax):\n",
    "    xx = np.linspace(-1, 11, 100)[:,None]\n",
    "    mu, var = m.predict_y(xx)\n",
    "    ax.plot(xx, mu, color, lw=2)\n",
    "    ax.fill_between(xx[:,0], mu[:,0] -  2*np.sqrt(var[:,0]), mu[:,0] +  2*np.sqrt(var[:,0]), color=color, alpha=0.2)\n",
    "    ax.plot(X, Y, 'kx', mew=2)\n",
    "    ax.set_xlim(-1, 11)\n",
    "\n",
    "f, ax = plt.subplots(3,2,sharex=True, sharey=True, figsize=(12,9))\n",
    "plot(m1, 'C0', ax[0,0])\n",
    "plot(m2, 'C1', ax[1,0])\n",
    "plot(m3, 'C2', ax[0,1])\n",
    "plot(m4, 'C3', ax[1,1])\n",
    "plot(m5, 'C4', ax[2,0])\n",
    "plot(m6, 'C5', ax[2,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the kernels and likelihoods, which show the fitted kernel parameters and noise variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPR\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n",
      "VGP\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n",
      "SVGP\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n",
      "SVGP\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n",
      "SGPR\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n",
      "GPRFITC\n",
      "  kernel lengthscale  = 1.0774\n",
      "  kernel variance     = 0.82561\n",
      "  likelihood variance = 0.16002\n"
     ]
    }
   ],
   "source": [
    "for m in models:\n",
    "    print(m.__class__.__name__)\n",
    "    print(\"  kernel lengthscale  = {:.5g}\".format(m.kern.lengthscales.value))\n",
    "    print(\"  kernel variance     = {:.5g}\".format(m.kern.variance.value))\n",
    "    print(\"  likelihood variance = {:.5g}\".format(m.likelihood.variance.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the likelihoods (or ELBOs):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPR                             -18.834361360056654\n",
      "VGP                             -18.834361360058967\n",
      "SVGP                            -18.834399989069947\n",
      "SVGP                            -18.834399989069947\n",
      "SGPR                            -18.834399989061836\n",
      "GPRFITC                         -18.83435478071552\n"
     ]
    }
   ],
   "source": [
    "for m in models:\n",
    "    print(\"{:30}  {}\".format(m.__class__.__name__, m.compute_log_likelihood()))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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